This study presents a pioneering effort to replicate human neuromechanical
experiments within a virtual environment utilising a digital human model. By
employing MyoSuite, a state-of-the-art human motion simulation platform
enhanced by Reinforcement Learning (RL), multiple types of impedance
identification experiments of human elbow were replicated on a musculoskeletal
model. We compared the elbow movement controlled by an RL agent with the motion
of an actual human elbow in terms of the impedance identified in
torque-perturbation experiments. The findings reveal that the RL agent exhibits
higher elbow impedance to stabilise the target elbow motion under perturbation
than a human does, likely due to its shorter reaction time and superior sensory
capabilities. This study serves as a preliminary exploration into the potential
of virtual environment simulations for neuromechanical research, offering an
initial yet promising alternative to conventional experimental approaches. An
RL-controlled digital twin with complete musculoskeletal models of the human
body is expected to be useful in designing experiments and validating
rehabilitation theory before experiments on real human subjects.

通过使用 MyoSuite 这个最先进的人体运动仿真平台，结合强化学习技术对人肘关节进行多个类型的阻抗识别实验，该研究初步探索了虚拟环境模拟在神经机械研究中的潜力，并提供了一种初步且有前景的替代传统实验方法的方案。